WEBVTT
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[Music]
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We're here with Jeanette
Wing. Jeanette, tell us
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a little bit about your background. You
have a very strong academic background.
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>> Yes I was at Carnegie Mellon
University for over 27 years,
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where I was Department Head twice.
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Before that I was actually at the
University of Southern California
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for two years.
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I got all my degrees at MIT.
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So I have been in academia for
basically my whole career, my
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whole professional life. I did have
a stint at the National Science
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Foundation for three years.
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That exposed me to how
DC really works.
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[Laughter]
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>> What was the path that led
you here to Microsoft?
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>> Well I have always had
friends at Microsoft.
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Certainly Rick Rashid was
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at Carnegie Mellon when I was
a very young faculty member.
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I saw him be swooped up by Microsoft
to start Microsoft Research.
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I have watched from afar in academia
of how Microsoft Research
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grew up over the 22, 23 years now.
So I've always been a friend
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of Microsoft.
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Also, I myself spent a sabbatical
at Microsoft Research in 2002,
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2003 and I had a great time.
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>> What were the things that interested
you most about Microsoft Research?
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>> Well, as an academic, as a researcher
myself there were two
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areas of research that
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overlapped in my interest with Microsoft
Research. One was the
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whole trustworthy computing effort
that was going on. That started
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with Bill Gates 2001 memo. In fact
that's what brought me to
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Microsoft Research in 2002, 2003
because I read that memo and
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I said, what does trustworthy computing
really mean? I figured
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the only way I'm going to find
out is if I come to Microsoft
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Research and work within Microsoft
Research to find out what
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TWC really means.
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The other area of interest, research
interest that overlap is
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actually the group that hosted me
was run by Jim Larus. It was
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the Software Productivity Tools Group
or something like that, SPT.
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At the time they were showing how
formal methods, which is my
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first research area of interest
in terms of theorem proving and
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model checking, and static analysis
could be used in practice,
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to scale. It was the heyday of
the SLAM Project. I was very
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lucky, I felt very lucky to be here
when all of that tech transfer
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was happening between Microsoft Research
and the rest of the company.
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>> What is computational
thinking?
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>> Computational thinking is the thought
processes for formulating
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a problem and expressing a solution
to that problem, in a way
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that a computer, a human or a machine
can effectively carry out.
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Now that's quite a mouth
full of a definition.
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I used words like effectively,
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solution, problem, expressing quite
carefully, because there
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are technical meanings to those terms.
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But let me put it more simply.
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I like to think of computational
thinking as just the thought
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processes that a computer scientist
would use in solving a problem.
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That begs the question of, what are
those ways a computer scientist
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approaches problem solving. Many
of the ways that we learn in
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computer science are just concepts,
but the most important thought
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process that all computer scientists use
in problem solving is abstraction.
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The ability to ignore all the irrelevant
complex details of the
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problem at hand, and look at a higher
level of what are the relevant
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parts of the problem that I need to
tackle, that I need to address?
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The ability to
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decompose a problem into smaller pieces,
solve the smaller pieces,
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and put together a solution to the
whole problem by putting together
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the solution to the smaller
pieces. Those kinds of
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thought processes of abstraction,
decomposition, composition
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are the bread and butter techniques
of a computer scientist kind
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of on a day to day basis.
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I strongly believe that anyone and
everyone can learn these concepts
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and learn how to use them, and
apply them in daily life.
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>> What age is it appropriate for a
child to begin thinking this way?
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How does one become better at
computational thinking?
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>> Well I have been advocating that
computational thinking is
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for everyone.
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I think that already from the undergraduate
program through graduate
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programs, and beyond
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people have already embraced the importance
of computational thinking.
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So now the challenge is to address
the K through 12 level.
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Now what would it mean to teach
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a kindergartner what computer science
is and what good computing
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can do for him or her when
you're five years old?
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>> Yeah.
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>> I think that's still an interesting
research question, not
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just for computer scientists, in
fact. But I would challenge
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the education scientist, the cognitive
scientist, the learning
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scientist to ask the question, what
kind of computing concepts
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would make sense to teach to a kindergartner?
I think there actually
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already programs out there that
address that question.
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So for instance there's a program
out there called CS Unplugged,
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which is teaching little kids all
about computer science without
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a computer.
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So for instance you can teach a
sorting algorithm by lining up
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a whole bunch of kids and you know
telling them they need to
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be sorted in a particular way,
and maybe they discover how to
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sort themselves, you know like
the height of the children.
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Maybe they discover on their own
a particular sorting algorithm.
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Then you can ask is there a more
efficient way you know and so
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they can actually discover on their
own the different kinds of
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sorting algorithms.
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>> Interesting, so this might date
me, but I remember when I was
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a child reading stories about how
big a computational problem,
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DNA sequencing was for example,
and that it might not even be
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done in my life time.
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In hindsight that's kind of laughable.
What are the big problems
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that we're facing today? What are
the big data problems that
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are the equivalent of that?
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>> Well the big data is clearly
a buzz word these days.
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I think that
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certainly every
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sector, every science, every engineering
discipline, every you
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know professional sector. When I say
sector law, medicine, humanities,
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and then all the sciences astronomy,
biology, and so on, they
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are facing this big data
problem in terms of
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generating lots of data with say
scientific instruments, like
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the large Hadron Collider.
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Collecting lots of data through
sensors, so we've got sensor
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nets that span the globe, all in real
time collecting lots of data.
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Then processing all this data
and then generating Metadata
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on top of that. You can imagine,
then let's just say that's for
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one scientific discipline. Now you'd
like to put it all together.
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You'd like
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different, in principle you'd like
all these streams of data
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to come together and all of us
to make some sense out of it.
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I like to talk about not just big
data, but I like to talk about
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from data to knowledge, to action.
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So from all this data, which is
just a lot of bits you'd like
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to actually extract some interesting
information, some knowledge,
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some semantics from this data. Then
from the representation of
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that knowledge, and it's usually
going to be visual, a human
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being or some Bot eventually will make
a decision, make an intelligent
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decision on that knowledge. So
the one example is if we look
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at some work for instance being
done at Microsoft Research in
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climate modeling. We've got
lots of data that different
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government agencies collect
in terms of weather and
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soil, and you know
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air, and wind, and so on. Imagine
all of this data being put
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together in a way that we can predict
what, when to plant what crop.
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So we have the capability. We have
the information sources.
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In the end you want someone to
make that decision you know we
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need to plant you know more soybeans
here, at this time of year,
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in this region.
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So that's where we're going with
the big data. Now right now
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we're so consumed with the big data
part, we're forgetting that
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what we really want is data
to knowledge to action.
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So by my saying we're so consumed
by the big data part, I mean
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that we're thinking all this data.
We have to store all this data.
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We have to process all this data.
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But in the end you want to represent
the knowledge that's inherent
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in all the bits, in a way that
a human being in the end will
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make some intelligent decision.
So we're, I think where we're
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going, I think we're very consumed
about big data now. But I
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think any, very soon we'll start
talking more about intelligent
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decision making, or visualization
of this data so that
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mere human beings can make these
decisions, can actually act
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on the knowledge that's represented
by the data.
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>> It's pretty standard for executives
to rave about the talent
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that they have under them. But,
you know we also like metrics.
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So if I challenge you and
say you know prove it.
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What are the things that you would
tell me about the researchers
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at MSR?
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>> Microsoft Research has a stellar
academic reputation.
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One of the standard metrics whether
we like it or not in terms
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of evaluating the quality of research
coming out of an organization,
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or the quality of the researchers
who produce the research is
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of course publications.
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Microsoft researchers are way up
there in terms of conference
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and journal publications, in
the highest quality venues.
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That's one metric, it's a stand
in actually for what I think
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is a more important metric in research.
But harder to quantify
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which is the impact of a person's
research, the impact of an
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organizations research.
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There again I still think that Microsoft
Research partly by the
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sheer size of Microsoft Research
as an organization has had a
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tremendous impact on the
advances of science.
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>> One of the groups within MSR is
the RiSE Team who've been very
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active in the developer community.
They've created and released
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some amazing tools. What's the
value of that group to MSR?
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>> Tremendous, so the RiSE Team
has had tremendous value both
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to Microsoft Research, but also to
the entire developer community.
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So I am quite familiar with the RiSE,
the kind of work that RiSE
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does, because that's actually my
home research area in formal
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methods and software engineering,
in programming languages.
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I just have to share with you one
story because I know one of
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the great contributions that RiSE
has made for the developer
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community has been code contracts.
If you look at what you would
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write in one of those contracts,
it would be requires clauses
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and insurers clauses, which are the equivalent
of pre-imposed conditions.
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In my very own Ph.D. thesis you
will see requires and insurers
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in the specification language that
I actually defined in my own
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thesis work. That wasn't new to me.
That was the kind of informal
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specifications we were actually
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asking undergraduates at MIT to
use in the Software Engineering
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course that I was a TA for. So way
back in, decades ago we were
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writing requires, insurers clauses.
We didn't have tool support
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the way you know the RiSE
Group has provided for
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developers, so we've come a long
way. But let me speak more
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specifically to the benefit of the
RiSE Group for both Microsoft
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Research and the developer community.
First of all they really
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are world class in terms of advancing
all the research areas
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I mention in terms of formal methods
and programming languages,
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static and dynamic analysis, testing,
verification, specification,
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and tool support thereof all.
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The, one of the things that really
impresses me about the RiSE
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Toolset is that they really
build on each other.
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So you have like Zed-3 as this constraint
solver on the bottom
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which is a core state of the art,
you know theorem improver.
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All these tools that sit on top
and work together, it's really
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impressive that this one team
has put this all together.
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All that is made available to the
developer community and the
00:16:13.070 --> 00:16:17.330
developer community at the same
time has been able to inspire
00:16:17.380 --> 00:16:21.750
the RiSE community, the RiSE Team
to work on research problems
00:16:21.800 --> 00:16:24.480
that I don't think they would have
thought of, had it not been
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for the feedback they got
from the developers.
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>> What are some of your favorite
projects at MSR?
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>> My, one of my current
favorites is actually
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the Worldwide Telescope
to GeoFlow example.
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Before I explain why that's, before
I explain that favorite I
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want to tell you why it's
one of my favorites.
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When I talk about research in computer
science, I like to talk
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about both the drivers of the questions
researchers ask in computer
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science, and also as I mentioned
earlier the impact that one
00:17:08.500 --> 00:17:12.910
can have in doing research in computer
science. So I like to
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talk about science drivers, technology
drivers, and societal drivers.
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So they're deep science questions
that researchers in computer
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science ask. They ask questions
like, what is computable; you
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know is this problem even
solvable by a computer?
00:17:31.530 --> 00:17:37.570
Those kinds of deep questions continue
to intrigue theoretical
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computer scientists, and as the
nature of what a computer is
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changes we have to re-ask that question.
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Technology drivers it's clear, as
technology advances we again
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have old questions that we have
to ask again because there are
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going to be new answers and
new questions to ask.
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Then I think what's really new
in the field of computing, or
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at least recently, is what I call
societal drivers. The societal
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grand challenges like healthcare,
energy, transportation, education
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that require advances in computer
science to actually help solve
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the problems in their sectors. I believe
that advances in computer
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science can actually transform the very
conduct of these other professions.
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So in healthcare for instance I
really believe that if we were
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far out thinking in terms of where
this country or where the
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world could be in terms of personalized
medicine and so on.
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To achieve that vision we are going to
need advances in computer science.
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So I think now instead of science,
technology, and society as
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being drivers of computer science
research. I think they're
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also measures of how much impact
we have had as a field. We have,
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through our research advanced science.
Certainly our understanding
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of computer science, but other sciences
as well, again biology,
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chemistry, material science, astronomy.
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Also through our research we have
certainly had an impact on
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technology, just look at Microsoft.
Look at all the Microsoft
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products and services, and devices
that we sell to our customers.
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A lot of the technology rests on advances
that we have made in research.
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Then finally there's society. I
like to think that through our
00:19:55.080 --> 00:19:59.200
reach, through our customers that
we reach through our technology.
00:19:59.250 --> 00:20:02.570
We certainly affect society
in a very direct manner.
00:20:03.320 --> 00:20:06.120
So getting back to the stories you
asked me. One of the reasons
00:20:06.170 --> 00:20:10.240
I like the Worldwide Telescope
story is it started as a pure
00:20:10.290 --> 00:20:16.010
science research story. Not even
computer science, it was about
00:20:16.060 --> 00:20:22.570
the stars, you know the galaxies.
You know I even questioned
00:20:23.020 --> 00:20:27.080
why was that research going on
in Microsoft Research? But it
00:20:27.130 --> 00:20:31.940
was going on anyway and so, and
of course it had to do with big
00:20:31.990 --> 00:20:33.550
data in some sense.
00:20:34.130 --> 00:20:39.280
So all of a sudden, not all of a sudden
but over time the research
00:20:39.330 --> 00:20:45.110
technology, the research ideas, the
ability to scale to the data
00:20:45.160 --> 00:20:51.080
that the Worldwide Telescope needed
to handle the ability for
00:20:51.130 --> 00:20:55.380
citizen scientists to actually contribute.
All that technology
00:20:55.840 --> 00:21:00.320
could actually have a very important
interesting application
00:21:00.370 --> 00:21:01.010
in Excel
00:21:02.440 --> 00:21:10.280
through PowerMap. Now
through this plugin
00:21:11.680 --> 00:21:19.660
people can look at crime statistics
in Chicago and policy makers
00:21:19.710 --> 00:21:24.600
can make intelligent decisions based
on the visualization that
00:21:24.650 --> 00:21:29.870
comes out of all this data packaged
in a way that again from
00:21:29.920 --> 00:21:35.110
data to knowledge to decision,
affects society. So this is my
00:21:35.160 --> 00:21:37.400
science, technology, society story.
00:21:39.040 --> 00:21:45.140
Another example along those lines
is some work that's more recently
00:21:45.190 --> 00:21:47.180
being done on urban computing.
00:21:47.810 --> 00:21:52.830
So this is work, one example
of this is work called U-Air
00:21:54.150 --> 00:21:59.870
that Yooshang our new
TR35 fellow from MSRA
00:22:01.110 --> 00:22:02.360
has been promoting.
00:22:03.160 --> 00:22:06.850
The idea is again, taking lots of
different data sources, whether
00:22:06.900 --> 00:22:08.140
its weather,
00:22:09.420 --> 00:22:13.490
road conditions, points
of interest in a city,
00:22:15.180 --> 00:22:19.250
the human mobility patterns,
00:22:20.920 --> 00:22:25.280
and putting all these data streams
together along with a few
00:22:25.330 --> 00:22:30.020
sensors that for instance are in
Beijing. Putting all the sensor
00:22:30.070 --> 00:22:34.540
data together to figure out what
is the air quality of not the
00:22:34.590 --> 00:22:38.160
city, but the street that
I live on in the city.
00:22:39.160 --> 00:22:44.920
So this is an example of multiple
data streams coming together.
00:22:44.970 --> 00:22:51.230
Using machine learning and other
data analytics algorithms to
00:22:52.410 --> 00:22:59.300
convey to the mere user, but also
to policy makers information
00:22:59.900 --> 00:23:06.650
that people can use to decide well
should I take this route to work?
00:23:06.700 --> 00:23:09.960
Should I go to the park because maybe
there's too much pollution
00:23:10.010 --> 00:23:15.120
out there and the air will be bad,
or for a policy maker to decide
00:23:15.980 --> 00:23:18.030
maybe we should actually
00:23:21.070 --> 00:23:25.430
focus on this particular neighborhood
to improve the air quality
00:23:25.480 --> 00:23:31.920
because of the readings that we have
from all this data? So that's,
00:23:31.970 --> 00:23:38.330
so that urban computing example is again,
you know science, technology,
00:23:38.380 --> 00:23:40.510
and society kind of coming together.
00:23:41.170 --> 00:23:44.690
So that's another example, there
are a lot of really fun things
00:23:44.740 --> 00:23:46.250
going on.
00:23:48.040 --> 00:23:51.120
There's, I mean I can talk, oh
I don't know how many stories
00:23:51.170 --> 00:23:57.400
do you want? I could talk; I mean
for instance I was just at
00:23:57.450 --> 00:23:59.920
Microsoft Research Asia a couple
weeks ago. They were showing
00:23:59.970 --> 00:24:04.300
me their newest work on hair modeling.
So it turns out that
00:24:04.350 --> 00:24:07.870
if you try to model the human being,
you know people work, have
00:24:07.920 --> 00:24:11.690
worked really hard on modeling
the face down to the level of
00:24:11.740 --> 00:24:15.660
you know the pores in the skin.
But it's really, really hard
00:24:15.710 --> 00:24:16.750
to model hair.
00:24:17.470 --> 00:24:21.150
So if you look at a lot of the state
of the art you'll see these
00:24:21.200 --> 00:24:25.570
heads without hair, because it's
much easier to model the skin,
00:24:25.620 --> 00:24:29.090
the eyebrows, and so on, I mean
the eyes and so on. But you
00:24:29.140 --> 00:24:32.820
won't see the hair, so what these
researchers have been doing
00:24:32.870 --> 00:24:35.220
is actually tackling that problem.
00:24:36.260 --> 00:24:39.470
Their most recent work is actually
to add a little dynamic.
00:24:39.520 --> 00:24:42.840
So now you can see the hair
like blowing in the wind.
00:24:42.850 --> 00:24:44.420
[Laughter]
00:24:44.370 --> 00:24:51.060
So they're like fun things that our
researchers work on as well.
00:24:51.110 --> 00:24:53.220
>> What's going to surprise
us in the future?
00:24:56.500 --> 00:24:59.070
>> If I told you it wouldn't
be a surprise.
00:24:59.120 --> 00:25:00.450
>> Yeah, I don't want
to be surprised.
00:25:00.720 --> 00:25:04.520
>> I think one of the
00:25:05.960 --> 00:25:12.870
areas that I see a lot of research
in right now, a lot of interest
00:25:12.920 --> 00:25:19.540
by the consumer, and where we're
going is in what Microsoft likes
00:25:19.590 --> 00:25:21.570
to call natural user interfaces.
00:25:22.110 --> 00:25:23.320
I think that
00:25:25.010 --> 00:25:33.690
we have already been seeing our
ability to get rid of the mouse
00:25:34.630 --> 00:25:38.060
and maybe the keyboard, at
least the physical one.
00:25:39.240 --> 00:25:41.980
I think with speech input,
00:25:43.770 --> 00:25:49.990
with gesture in terms of interacting
with devices, we are going
00:25:50.040 --> 00:25:56.080
to more naturally interact with
devices, with services,
00:25:57.320 --> 00:25:59.970
and then of course with each other.
00:26:00.510 --> 00:26:02.430
>> Thank you so much for talking
to us today Jeanette.
00:26:02.480 --> 00:26:03.220
>> Thank you.